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This project forked from geangohn /recsys-tutorial
Tutorials and examples of various recommender systems in industrial applications
License: Apache License 2.0
recsys-tutorial-1's Introduction
implicit : full data & model pipeline, article
LightFM : article
How to build Item2Vec (or W2V) for item recommendations in retail
OK.ru : graph based recsys, article
HH.ru : classic 2 level model of search at hh.ru, article
Okko competition : classic 2 level model, article
Yandex.Dzen : fit ALS -> fit Catboost on warm embeddings to predict warm&cold embeddings, 15-25min in video
TikTok : No use of popularity features! post
Instagram : Insights on candidate generation articles
DoorDash : Store2Vec as a feature in recommendations
Pinterest : Multi-taste user embeddings
AirBnb : Hotel2Vec with novel positive samples approach
How to use W2V and FastText for search: Query2Vec
Avito : FAISS for fast similar embedding search
Similar vectors search with Nmslib (HNSW - hierarchical navigable small world), FAISS (embeddings space K-means clustering + Product quantizer) and Annoy (divides embeddings space with a binary tree)
ElasticSearch basics
DoorDash Elasticsearch meets logistic regression
Avito : Recommending additional item - upsell with advanced W2V